A new method for load matching in multimode microwave feating applicators based on the use of dielectric layer superposition

Usage of a dielectric multilayer around a dielectric Sample is studied as a means for improving the efficiency in multimode microwave- heating cavities. The results show that by using additional dielectric constant layers the appearance of undesired reflections at the sample-air interface is avoided and higher power -absorption rates within the sample and high -efficiency designs are obtained

Depth wise variation of microbial load in the soils of midland region of Kerala: a function of important soil physicochemical characteristics and nutrients

Soil microorganisms play a main part in organic matter decomposition and are consequently necessary to soil ecosystem processes maintaining primary productivity of plants. In light of current concerns about the impact of cultivation and climate change on biodiversity and ecosystem performance, it is vital to expand a complete understanding of the microbial community ecology in our soils. In the present study we measured the depth wise profile of microbial load in relation with important soil physicochemical characteristics (soil temperature, soil pH, moisture content, organic carbon and available NPK) of the soil samples collected from Mahatma Gandhi University Campus, Kottayam (midland region of Kerala). Soil cores (30 cm deep) were taken and the cores were separated into three 10-cm depths to examine depth wise distribution. In the present study, bacterial load ranged from 141×105 to 271×105 CFU/g (10cm depth), from 80×105 to 131×105 CFU/g (20cm depth) and from 260×104 to 47×105 CFU/g (30cm depth). Fungal load varies from 124×103 to 27×104 CFU/g, from 61×103 to110×103 CFU/g and from 16×103 to 49×103 CFU/g at 10, 20 and 30 cm respectively. Actinomycetes count ranged from 129×103 to 60×104 CFU/g (10cm), from 70×103 to 31×104 CFU/g (20cm) and from 14×103 to 66×103 CFU/g (30cm). The study revealed that there was a significant difference in the depthwise distribution of microbial load and soil physico-chemical properties. Bacterial, fungal and actinomycetes load showed a decreasing trend with increasing depth at all the sites. Except pH all other physicochemical properties showed decreasing trend with increasing depth. The vertical profile of total microbial load was well matched with the depthwise profiles of soil nutrients and organic carbon that is microbial load was highest at the soil surface where organics and nutrients were highest

Acoustic Structure Interaction For Floating Structure Under Moving Load

The present thesis concentrates largely on sound radiation from floating structure due to moving load

Reducing Vibrio load in Artemia nauplii usingantimicrobial photodynamic therapy: a promising strategy to reduce antibiotic application in shrimp larviculture

We propose antimicrobial photodynamic therapy (aPDT) as an alternative strategy to reduce the use of antibiotics in shrimp larviculture systems. The growth of a multiple antibiotic resistant Vibrio harveyi strain was effectively controlled by treating the cells with Rose Bengal and photosensitizing for 30 min using a halogen lamp. This resulted in the death of > 50% of the cells within the first 10 min of exposure and the 50% reduction in the cell wall integrity after 30 min could be attributed to the destruction of outer membrane protein of V. harveyi by reactive oxygen intermediates produced during the photosensitization. Further, mesocosm experiments with V. harveyi and Artemia nauplii demonstrated that in 30 min, the aPDT could kill 78.9% and 91.2% of heterotrophic bacterial and Vibrio population respectively. In conclusion, the study demonstrated that aPDT with its rapid action and as yet unreported resistance development possibilities could be a propitious strategy to reduce the use of antibiotics in shrimp larviculture systems and thereby, avoid their hazardous effects on human health and the ecosystem at large.

Short-Term Load Forecast Of A Low Loadfactor Power System For Optimization Of Merit Order Dispatch Using Adaptive Learning Algorithm

Short term load forecasting is one of the key inputs to optimize the management of power system. Almost 60-65% of revenue expenditure of a distribution company is against power purchase. Cost of power depends on source of power. Hence any optimization strategy involves optimization in scheduling power from various sources. As the scheduling involves many technical and commercial considerations and constraints, the efficiency in scheduling depends on the accuracy of load forecast. Load forecasting is a topic much visited in research world and a number of papers using different techniques are already presented. The accuracy of forecast for the purpose of merit order dispatch decisions depends on the extent of the permissible variation in generation limits. For a system with low load factor, the peak and the off peak trough are prominent and the forecast should be able to identify these points to more accuracy rather than minimizing the error in the energy content. In this paper an attempt is made to apply Artificial Neural Network (ANN) with supervised learning based approach to make short term load forecasting for a power system with comparatively low load factor. Such power systems are usual in tropical areas with concentrated rainy season for a considerable period of the year